Configure model serving, access control and operations around your data and usage requirements
This request is for 12 boxes of gloves. Can it be dispatched?
Check approval and delivery address as well as the item and quantity. The available material has no approval record. Ask the dispatch approver to review it.
Dispatch manual §2 — Verify item, quantity, delivery address and approval before dispatch.
The customer wants a new delivery date but gave no order number.
Without the order and dispatch status, the delivery change cannot be confirmed. Prepare a request for the order number first.
Customer-service manual §4 — Do not confirm a change before identifying the order and checking dispatch status.
Assess models, hardware and serving against data retention and expected load. Connect access control, model versions, resource usage and recovery to operations.
Before listing features, we define the inputs, decisions and next actions in a real working situation.
An illustrative scenario. Specific processing rules are agreed for the project.
A team wants AI to work with internal documents.
Serve the model in the agreed environment and monitor use
When data location matters or models need to run on an internal network
Specific features, integrations and operating support are agreed in the project proposal
See the scope, role and supplied interfaces for each project
Original planning & project documents
Original planning & project documents
Details for technical reviewers. Architecture and acceptance criteria are agreed for the actual environment and project scope.
Authenticated requests pass through quotas and a queue into an inference pool. Capacity includes weights and KV cache; observation distinguishes time to first token from latency between output tokens.
Concrete design decisions and acceptance scenarios to agree for the project.
ImplementationContext, concurrency, weights, KV-cache budget
ValidationMixed long- and short-context load
ImplementationAuth gateway, artifact import, ACL retrieval
ValidationDeny direct inference and unauthorized retrieval
ImplementationTTFT, TPOT, queueing, cancellation policy
ValidationOverload, disconnected clients, model replacement
Technology choices and acceptance criteria depend on discovery, integrations, and agreed scope. We define reproducible acceptance scenarios and handover evidence for the operating environment.
From visible features to failure conditions,
we work through the decisions before launch.
Compare Korean quality, context needs, structured output, tool calling, and deployment licenses. Evaluate candidates against internal tasks and failure cases, including quality changes after quantization. Assess private deployment against hosted APIs using security requirements, total operating cost, and control over changes, rather than assuming local hosting is always preferable.
Separate weights, KV cache, activations, and runtime headroom. Model input and output lengths together with concurrency distributions. Assess batching, prefix caching, and quantization options in an engine such as vLLM, then measure the memory, latency, and throughput tradeoffs under a representative request mix instead of quoting a GPU-only capacity estimate.
Place authentication and model quotas at a gateway and restrict direct access to inference nodes. Specify how model artifacts, container images, and secrets enter and update the environment. Retrieval remains a separate permission-aware layer. For isolated networks, include artifact transfer, licensing, and security-update procedures in the deployment design.
Observe queueing, time to first token, inter-token latency, end-to-end response time, and GPU memory separately. Define request-length and concurrency limits, timeouts, cancellation propagation, and overload responses. Apply the same evaluation set and load profile during model changes and document how to route back to the previous model.
Understand your goals, workflows, and operating environment.
Define features, priorities, and integration requirements.
Build the agreed scope and review progress together.
Prepare testing, deployment, and operational handover.
Tell us what you want to build or improve
Our team will review your request and reply by email
